Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2001-07-19
2003-04-29
Vo, Hieu T. (Department: 3747)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C600S410000, C600S424000, C324S307000
Reexamination Certificate
active
06556855
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention is directed to a method for the implementation of a perfusion measurement with magnetic resonance imaging.
2. Description of the Prior Art
Magnetic resonance technology is a known technique for acquiring images of an inside of a body of an examination subject. Rapidly switched gradient fields that are generated by a gradient system are superimposed on a static basic magnetic field in a magnetic resonance apparatus. The magnetic resonance apparatus also has a radio-frequency system that emits radio-frequency signals into the examination subject for triggering magnetic resonance signals and that picks up the generated magnetic resonance signals. Image datasets and magnetic resonance images are produced on the basis thereof.
In one embodiment of functional magnetic resonance imaging, image datasets of a region of an examination subject to be imaged are generated in chronological succession with an identical location coding. Thereafter, a retrospective motion correction of the image datasets is implemented. Differences between the image datasets that are the result of a positional change of the region to be image relative to the apparatus during the temporal succession being capable of being determined and corrected by means of this motion correction. A method for determining positional change from image datasets registered in chronological succession is based on a description of an arbitrary rigid body movement in three-dimensional space with six motion parameters; three parameters identify translations and three parameters identify rotations. These parameters are, for example, rotated in a column vector {right arrow over (q)}. The values of all voxels or of selected voxels of a first image dataset and of a second image dataset that has been produced temporally following the first are rotated in a coinciding sequence in a first column vector {right arrow over (x)} and a second column vector {right arrow over (y)}. The following equation, which is based on a Taylor expansion of the first order is solved by an iterative method, for example a Gauss-Newton iteration method, for determining a positional change between the respective registration times of the first and second image dataset, i.e. for determining the motion parameters:
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A more detailed description of this procedure is available in the book by R. S. J. Frackowiak et al.,
Human Brain Function
, Academic Press, 1997, particularly Chapter 3, pages 43-48, and the article by K. J. Friston et al., “Movement-Related Effects in fMRI Time-Series”, Magnetic Resonance in Medicine 35 (1196), pages 346-355.
Moreover, the latter article notes that not all unwanted signal differences as a result of movement can be eliminated even given an optimum back-rotation or, respectively, back-shift of the image datasets with respect to a reference image dataset. The cause of this is that, following a positional change of the region to be imaged, gradient fields and radio-frequency fields act differently on specific volume regions of the region to be imaged compared to its initial position given unmodified location coding. Excitation, resonance and relaxation properties of the volume regions change as a result. Thus, the signal behavior of these volume regions is modified not only for an immediately successively registered image dataset but also persistently for further image datasets to be registered. The article by K. J. Friston et al. proposed an approximation method with which these latter, motion-caused signal differences also can be filtered out of image datasets following the generation of the image datasets.
In another method for image dataset-based acquisition of positional changes, all or specific, selected points of a first image dataset described in k-space, and of a second image dataset that has been generated following the first in time, are compared. The method is based on the fact that, due to a positional change between the registration times of the two image datasets, translations and/or rotations of the region to be imaged are reflected in a modification of phase and/or amount of the data points given a comparison of data points that are identically arranged within the two image datasets. For example, embodiments of the aforementioned method are described in greater detail in the articles by L. C. Maas et al., “Decoupled Automated Rotational and Translational Registration for Functional MRI Time Series Data: The DART Registration Algorithm”, Magnetic Resonance in Medicine 37 (1997), pages 131 through 139, as well as in the article by Q. Chen et al., “Symmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 12 (1994), pages 1156 through 1168.
Another approach for avoiding unwanted, motion-caused differences in a functional magnetic resonance imaging does not correct the image datasets retrospectively but implements a prospective motion correction during an executive sequencing of the functional magnetic resonance imaging. To that end, positional changes of the region to be imaged that may occur from image dataset to image dataset are acquired, for example, by orbital navigation echos and a location coding is correspondingly adapted during the executive sequence. An orbital navigation echo is a magnetic resonance signal that is characterized by a circuitous k-space path and that is generated by a specific navigator sequence. Positional changes can be determined on the basis of orbital navigator echos that are generated at different points in time. To that end, for example, the navigator sequence is implemented for every generation of an image dataset, and a navigator echo is registered that is compared to a reference navigator echo for the motion correction. This is described in detail in, for example, the article by H. A. Ward et al., “Real-Time Prospective Correction of Complex Multiplanar Motion in fMRI”, Proc. of ISMRM 7 (1999), page 270.
In another known method, positional changes of the region to be imaged are optically acquired using optical reflectors, that are monitored by an optical acquisition system as to their position, attached to the region to be imaged. Further details thereof are explained, for example, in the article by H. Eviatar et al., “Real Time Head Motion Correction for Functional MRI”, Proc. of ISMRM 7 (1999), page 269. Further U.S. Pat. No. 5,828,770 and U.S. Pat. No. 5,923,417 are referenced thereto.
In another known method for prospective motion correction, the methods described in the initially cited book by R. S. J. Frackowiak and article by K. J. Friston are utilized for determining positional changes from image datasets registered in temporal succession. Further details thereof are described in the article by S. Thesen et al., “Prospective Acquisition Correction for Head Motion with Image-based Tracking for Real-Time fMRI”, Proc. of ISMRM 8 (2000), page 56.
In a perfusion measurement with magnetic resonance technique, a number of volume datasets of same region to be imaged in an examination subject, for example a brain of a patient, are registered in an optimally fast time sequence. This occurs regardless of whether a contrast agent is administered. A determination about a local perfusion can be acquired from a time change of a value of a voxel that is identically positioned within the registered volume datasets. When a positional change of the region to be imaged occurs during the registration of the volume datasets given an identical location coding, then this leads to a translation and/or rotation of the individual v
Schiff & Hardin & Waite
Siemens Aktiengesellschaft
Vo Hieu T.
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